
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "106"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 104 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 100 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.674172	-0.461864	0.298183	0.628642	5.329603	13.825172	-0.430512	-0.830000	0.8113	0.8150	0.2334	11.223695	12.050483
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	2.321454	1.356127	-0.129910	-0.004651	1.770868	0.511569	-0.284215	-0.750474	0.7449	0.7428	0.2432	2.322253	2.444281
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.478161	-1.200390	2.429570	2.141990	1.171977	0.677594	0.895675	-0.047788	0.0295	0.0320	0.0014	nan	nan
2459591	digital_ok	0.00%	0.00%	0.00%	0.00%	1.62%	0.00%	2.292628	1.477638	0.167386	-0.296609	1.828109	1.698077	0.209322	-0.383062	0.6873	0.6974	0.3574	1.840004	1.748525
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.797620	1.519979	1.101704	0.740130	5.273964	4.199502	0.293340	-0.001528	0.6896	0.6963	0.3517	5.839277	5.888147
2459589	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.006410	1.178797	2.333940	0.557325	3.072148	19.218252	-0.140714	-0.470011	0.7339	0.7271	0.2732	5.334802	5.529248
2459588	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	1.651562	0.476569	-0.168449	0.650386	1.771438	0.777998	-0.374721	-1.057311	0.7913	0.7756	0.2112	3.117452	2.614609
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	2.140041	1.087430	-0.197767	0.427109	-1.598116	1.250181	-4.675000	-0.603252	nan	nan	nan	14.947995	17.358240
2459586	digital_ok	0.00%	0.00%	0.00%	0.00%	12.43%	5.41%	1.108727	0.659651	-0.448475	-0.297527	1.767914	1.440296	-0.119344	-0.788595	0.6846	0.6914	0.3477	1.506100	1.493998
2459585	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.470462	-0.358284	0.213647	0.392208	0.094447	-0.627312	0.558736	-0.309066	0.0299	0.0320	0.0009	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	15.853077	15.303260	4.494447	2.445203	14.535587	6.378398	29.895607	30.582084	0.6694	0.6752	0.3635	57.983252	33.407619
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.506988	1.975978	1.523545	-0.079131	5.798201	3.084413	-0.083565	-0.036864	0.6966	0.6991	0.3689	7.539375	4.315430
2459582	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.892888	1.651879	1.452030	-0.270965	6.339517	3.657035	-0.787943	-0.240502	0.6927	0.6980	0.3707	3.020709	3.108811
2459581	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.298631	2.305483	0.511062	-0.460453	4.750085	3.030299	-0.137142	-0.230534	0.6994	0.7033	0.3674	3.231285	3.145268
2459580	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.782279	1.684056	1.429446	-0.299046	7.029599	3.552876	-0.445791	-0.288402	0.6976	0.7010	0.3669	3.122292	2.907507
2459579	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.872448	1.599396	1.963779	-0.181727	6.587009	3.565512	-0.622997	-0.060029	0.6950	0.6997	0.3699	4.512663	4.895629
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.951787	-0.643781	1.720728	1.795842	1.985729	-0.006685	0.606841	-0.847431	0.0288	0.0267	0.0012	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	2.008783	1.628846	0.639354	-0.634584	3.948697	2.273618	0.068750	0.125187	0.6950	0.6991	0.3696	1.521229	1.528249
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	0.00%	1.511803	1.032190	0.009752	-0.521228	3.539396	1.071940	-0.250532	-0.521631	0.7163	0.7214	0.3665	1.424938	1.468987
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.681807	1.276196	0.716179	-0.670962	4.658299	2.992226	0.461493	-0.650217	0.7745	0.7760	0.3093	4.256436	4.773149
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.857171	1.285308	0.342224	-0.855827	1.757738	-0.491816	0.186760	-0.631861	0.7174	0.7246	0.3566	1.506768	1.539550
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.346207	0.814230	2.371773	0.283682	5.017759	3.645339	-0.339554	0.100795	0.7140	0.7176	0.3600	4.489617	4.245892
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	-0.763158	-0.647654	1.699338	1.590335	6.089816	2.106235	0.658099	-0.606656	0.0290	0.0275	0.0010	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.315616	0.048918	0.371714	-0.616275	0.551028	6.116467	-0.072078	-0.478589	0.7899	0.7873	0.2206	4.610968	4.267701
2459566	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.589078	1.555237	1.096395	-0.485520	3.813905	2.169170	-0.561273	-0.735439	0.0568	0.0561	0.0050	1.262389	1.257112
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	4287.746435	4287.746435	4026.123085	4026.123085	-0.635090	-0.635090	-1.849535	-1.849535	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	0.00%	0.00%	0.00%	0.00%	5.20%	1.73%	0.154091	0.358856	-0.399845	-0.555572	1.978780	-1.393026	0.173175	0.001373	0.7260	0.7239	0.3747	1.577945	1.459403
2459563	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.788214	1.500081	-0.515073	0.027949	5.407333	4.146600	-0.109427	0.094144	0.7157	0.7195	0.3722	3.847238	3.524582
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.465029	-0.121118	-1.108199	-0.340866	-1.159551	-0.687484	0.213154	0.143801	0.7229	0.7279	0.3636	1.696632	1.597343
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.279673	-0.198324	-1.589455	-0.738886	-1.849505	-1.480774	-0.549055	-0.230525	0.7224	0.7301	0.3623	1.613758	1.589197
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	-0.673033	-0.342468	-1.226522	-0.666438	-1.028406	-1.103694	-0.172526	0.112472	0.7109	0.7176	0.3761	1.566581	1.510601
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.91%	0.00%	-0.089171	0.289799	-1.109615	-0.494317	-0.513602	-1.141220	-0.159235	-0.072952	0.7203	0.7230	0.3647	1.553469	1.469790
2459558	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.117542	1.755958	2.079659	-0.158825	5.426143	2.480674	-0.097792	-0.237104	0.7254	0.7248	0.3701	3.766688	3.499689
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0296	0.0280	0.0015	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	2.235969	1.783181	1.852044	-0.608864	3.524273	2.302381	-0.566448	0.149021	0.7226	0.7223	0.3683	1.586726	1.560629
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7201	0.7224	0.3772	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7223	0.7223	0.3757	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.113280	1.602242	1.326506	0.034630	4.759667	20.850894	-0.320725	0.351935	0.7261	0.7129	0.3742	9.112762	7.843845
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	-0.711378	-0.567147	-1.131170	-0.855619	-1.288830	-1.665214	-0.144540	-0.054376	0.7202	0.7094	0.3733	1.625993	1.557208
2459550	digital_ok	-	99.18%	99.18%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0449	0.0436	0.0007	nan	nan
2459549	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.399667	1.200101	0.861597	0.157011	5.202115	3.524129	-0.538974	0.332646	0.7219	0.7130	0.3792	3.549420	3.251924
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	1.772906	0.568465	0.021922	-0.822405	2.328994	1.453166	0.061191	-0.201932	0.7732	0.7631	0.3298	1.689149	1.623308
2459541	digital_ok	0.00%	1.79%	1.79%	0.00%	21.88%	0.00%	1.451313	1.132994	1.231952	-0.512627	1.022183	-0.022215	-0.387621	-1.274467	0.7638	0.7473	0.3073	1.634703	1.590951
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	0.219738	-0.596147	-1.555514	-1.072407	3.282182	2.413014	-0.682276	-0.486372	0.7731	0.7611	0.3196	1.479734	1.447181
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	1.793143	-0.385459	-0.678580	-0.712570	1.649147	1.334753	-0.375917	-0.587425	0.7579	0.7449	0.3891	1.637378	1.493029
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	0.232516	-0.736434	-1.267127	-0.980359	2.943986	2.875609	-0.325168	0.136356	0.8033	0.7771	0.3365	1.648626	1.545899
2459534	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.632328	0.264593	1.463474	0.811917	5.304947	2.940199	-0.490796	0.121998	0.7455	0.7333	0.3776	3.298044	3.179211
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.661614	0.813714	1.420727	0.536491	5.552811	2.231115	-0.229647	-0.125895	0.7457	0.7296	0.3769	4.530539	4.029511
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.638800	-1.245556	-0.910067	-0.759333	-0.815266	-0.949397	0.158255	0.581321	0.7626	0.7417	0.3801	1.575213	1.575795
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.875628	0.936745	1.730518	1.279907	0.099573	-0.374705	-0.810400	-0.563552	0.9188	0.9487	0.2701	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	0.905131	0.266799	0.632557	0.491231	0.672530	-0.828642	-0.090669	0.821902	0.7199	0.7262	0.3306	1.796587	1.775951
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-1.017269	-0.575722	0.995249	1.279431	0.257653	0.095746	1.174227	-0.514498	0.0322	0.0345	0.0010	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.492011	0.778604	0.641587	0.592152	0.811481	0.563475	-0.132429	0.191958	0.9291	0.9591	0.1527	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.191374	0.719088	-0.580573	-0.756067	3.885249	0.896897	2.369393	0.201044	0.8913	0.8776	0.1753	4.128140	4.443085
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	0.559764	1.086736	-0.610545	0.170448	0.948770	0.183352	2.080531	-0.018666	0.8949	0.8860	0.4106	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	1.673378	2.113695	1.040424	1.379624	2.867105	1.609738	-0.560142	-0.436359	0.8425	0.8781	0.3642	0.000000	0.000000
2459500	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.999998	2.860769	1.602251	1.908079	1.464142	1.735493	-0.490171	-0.721387	0.8758	0.8981	0.3698	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	1.757234	2.431511	1.689523	1.838137	1.346934	1.319899	-0.012622	-0.219016	0.8781	0.9361	0.1916	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	1.980930	2.566914	1.064599	1.228554	0.513564	0.691648	0.291109	0.264489	0.8860	0.9020	0.3950	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.271354	2.832322	0.841532	1.017615	-0.224368	0.316526	-0.821964	-0.457119	0.1070	0.1110	0.0128	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.759048	2.745317	0.642633	1.022453	-0.647837	0.081419	-0.047670	-0.567200	0.1053	0.0945	0.0104	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.190076	2.324113	0.394777	0.984001	-0.470088	0.304233	0.054092	-0.602383	0.1144	0.1129	0.0154	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	2.761033	3.182730	1.562306	1.701560	0.276278	0.394427	-0.590929	-0.438316	0.1113	0.1187	0.0155	0.000000	0.000000
2459492	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.701524	1.515291	-1.315798	-0.639705	6.107959	2.040991	1.340196	4.282818	0.0787	0.1211	0.0093	0.000000	0.000000
2459491	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.433066	1.177503	-0.350377	-1.465894	5.655583	0.638075	1.012211	-0.617763	0.0925	0.0932	0.0106	0.000000	0.000000
2459490	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.370848	1.371943	0.379274	-1.797332	4.497250	1.715310	3.751188	1.279435	0.0749	0.0811	0.0071	0.000000	0.000000
2459489	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.305613	1.031169	-0.933628	-1.484950	8.429315	4.466317	3.906302	-0.191105	0.0720	0.0864	0.0064	0.000000	0.000000
2459488	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	1.705675	1.127425	-1.113918	-0.337143	4.848104	2.763567	2.559601	-0.003858	0.0789	0.0991	0.0073	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 106 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 106, Node 9:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 106: 2459628

Antenna 106: 2459627

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶